Massive Online Analysis

Updated on Dec 04, 2023

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Developer(s) University of Waikato Repository github.com/waikato/moa Type Machine Learning		Stable release 2014.11 / 2014/11/30 Operating system Cross-platform
License GNU General Public License

MOA (Massive Online Analysis) is a free open-source software specific for Data stream mining with Concept drift. It is written in Java and developed at the University of Waikato, New Zealand.

Description

MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the Graphical User Interface (GUI), the command-line, and the Java API. MOA contains several collections of machine learning algorithms:

Classification

Bayesian classifiers

Naive Bayes

Naive Bayes Multinomial

Decision trees classifiers

Decision Stump

Hoeffding Tree

Hoeffding Option Tree

Hoeffding Adaptive Tree

Meta classifiers

Bagging

Boosting

Bagging using ADWIN

Bagging using Adaptive-Size Hoeffding Trees.

Perceptron Stacking of Restricted Hoeffding Trees

Leveraging Bagging

Online Accuracy Updated Ensemble

Function classifiers

Perceptron

Stochastic gradient descent (SGD)

Pegasos

Drift classifiers

Multi-label classifiers

Active learning classifiers

Regression

FIMTDD

AMRules

Clustering

StreamKM++

CluStream

ClusTree

D-Stream

CobWeb.

Outlier detection

STORM

Abstract-C

COD

MCOD

AnyOut

Recommender systems

BRISMFPredictor

Frequent pattern mining

Itemsets

Graphs

Change detection algorithms

These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.

MOA supports bi-directional interaction with Weka (machine learning). MOA is free software released under the GNU GPL.

References

Massive Online Analysis Wikipedia

(Text) CC BY-SA